October 2026

Closing the GapWhy AI is ready while most companies are still just getting started

Almost every week, a new language model arrives with even better benchmarks. Many companies watch these updates closely and wait for the next major breakthrough.

But the real challenge lies somewhere else. Even if every AI lab in the world froze its development today, companies would still have enough untapped potential for the next five years.

The field has a name for this: capability overhang. It describes the enormous gap between what artificial intelligence can already do technically and how people and organizations actually use the technology in their daily work.

The technology is extremely advanced. Practical adoption inside companies, however, is often still in its early stages.

What the numbers show: A huge engine in first gear

Current data makes this gap clear. According to Bitkom, 57 percent of German companies now use artificial intelligence. On paper, that looks like successful adoption.

Look deeper and the picture changes. In McKinsey's current “The State of AI 2026” study, almost every second German decision-maker says their company has already scaled AI. At the same time, 43 percent of these companies cannot quantify AI's contribution to their operating result. The technology is spreading, but it reaches the depth of value creation far too rarely.

The global picture is similar. A Boston Consulting Group study of more than 1,250 companies shows that only 5 percent achieve real, scaled business value through AI. A full 60 percent see barely measurable revenue growth or cost reductions despite significant investment.

The capability overhang becomes particularly tangible at the individual employee level. OpenAI analyzed its own usage data. Power users, the most active five percent of users, use complex reasoning and analysis features seven times more often than the average user on the exact same plan.

Even among paying enterprise customers, 19 percent have never used integrated data analysis. Fourteen percent have never used a reasoning model for deeper logical tasks. The tools are available, but remain untouched in daily work.

Why the gap is so large in everyday work

When my co-founders and I work with companies and talk to teams, we see the same patterns again and again. The models can analyze complex reports, review strategies, write code, and manage multi-step processes. In daily office work, most users still type short questions or ask AI to rewrite an email.

There are three main reasons:

Old habits in new systems: Many employees use AI like a classic search engine. They enter three keywords and expect a finished result. Modern models become truly powerful when users provide context and delegate entire task packages.
Friction in the tools: A global Cognizant study of more than 10,000 employees shows that 80 percent would like to use AI much more, but are held back in everyday work. Up to 82 percent are dissatisfied with the AI tools provided by their employer because they are too rigid or do not fit real tasks.
Unchanged workflows: Many companies simply place AI on top of existing processes. A chatbot at the edge of the screen does not change a business model. Real value appears when teams rethink their workflows from the ground up.

The decisive lever: enablement inside the team

Good software alone will never close the capability overhang. If a company simply buys licenses and distributes them to employees, very little usually happens after the initial curiosity. The key to real results is enablement.

Companies must actively enable their employees to work with the technology. In our own companies and in the workshops we run for clients, we see every day what happens when teams finally connect the dots.

Enablement primarily means providing orientation and confidence. Many employees are unsure what they are legally allowed to enter into a system. Others simply do not know which tasks from their daily to-do list a language model can already take over.

Teams need pragmatic approaches:

Concrete use cases from the department: General theory helps nobody. Procurement teams need to see how to compare supplier offers. Marketing teams need to learn how campaign concepts can be created on demand.
Room to experiment: Teams need dedicated time and formats to build workflows and share experiences. When a colleague shows how they save three hours every week on preparing offers, the whole department takes notice.
Continuous support: AI evolves extremely quickly. One workshop a year is not enough. Companies need internal multipliers who test new features and bring them directly to the team.

From chatting to doing: How companies close the gap

The five percent of companies already generating significant economic value from AI, according to BCG, take exactly this step. They achieve five times higher revenue growth and three times higher cost reductions than average.

These pioneers stop treating AI as a text tool for simple support tasks. They shift usage toward complex tasks, data analysis, and multi-step workflows. They invest deliberately in workforce capabilities and redesign core processes in sales, IT, and product development.

The advantage is created through execution

Today's AI models already exceed what 95 percent of companies actually use in daily work. Waiting for the next model update will not move any company forward.

Economic advantage will be created in the coming years through consistent execution and the enablement of your own people. Companies that integrate existing technology deeply into their processes and enable their teams today will build an advantage that competitors will struggle to catch.